Abstract
Humans have mastered the skill of creativity for many decades. The process of replicating this mechanism is introduced recently by using neural networks which replicate the functioning of human brain, where each unit in the neural network represents a neuron, which transmits the messages from one neuron to other, to perform subconscious tasks. Usually, there are methods to render an input image in the style of famous art works. This issue of generating art is normally called nonphotorealistic rendering. Previous approaches rely on directly manipulating the pixel representation of the image. While using deep neural networks which are constructed using image recognition, this paper carries out implementations in feature space representing the higher levels of the content image. Previously, deep neural networks are used for object recognition and style recognition to categorize the artworks consistent with the creation time. This paper uses Visual Geometry Group (VGG16) neural network to replicate this dormant task performed by humans. Here, the images are input where one is the content image which contains the features you want to retain in the output image and the style reference image which contains patterns or images of famous paintings and the input image which needs to be style and blend them together to produce a new image where the input image is transformed to look like the content image but “sketched” to look like the style image.
Highlights
Deep Convolutional Nets Learning Classification for Artistic Style TransferE process of replicating this mechanism is introduced recently by using neural networks which replicate the functioning of human brain, where each unit in the neural network represents a neuron, which transmits the messages from one neuron to other, to perform subconscious tasks
A decade ago, when machine learning was an emerging application of artificial intelligence providing the ability to automate learning process from foregoing experiences without being explicitly programmed, the only limitation was assumed that a good computer program can never replace a human in creativity [1]
As the exploration in the field grew, this gave rise to many other subfields like deep learning, which threw the limelight on the solution for replacing humans for their creativity or their process of recognizing objects or people [2]
Summary
E process of replicating this mechanism is introduced recently by using neural networks which replicate the functioning of human brain, where each unit in the neural network represents a neuron, which transmits the messages from one neuron to other, to perform subconscious tasks. There are methods to render an input image in the style of famous art works. Is issue of generating art is normally called nonphotorealistic rendering. While using deep neural networks which are constructed using image recognition, this paper carries out implementations in feature space representing the higher levels of the content image. Deep neural networks are used for object recognition and style recognition to categorize the artworks consistent with the creation time. Is paper uses Visual Geometry Group (VGG16) neural network to replicate this dormant task performed by humans. The images are input where one is the content image which contains the features you want to retain in the output image and the style reference image which contains patterns or images of famous paintings and the input image which needs to be style and blend them together to produce a new image where the input image is transformed to look like the content image but “sketched” to look like the style image
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